Open-set semi-supervised learning (OSSL) is a realistic setting of semi-supervised learning where the unlabeled training set contains classes that are not present in the labeled set. Many existing OSSL methods assume that these out-of-distribution data are harmful and put effort into excluding data from unknown classes from the training objective. In contrast, we propose an OSSL framework that facilitates learning from all unlabeled data through self-supervision. Additionally, we utilize an energy-based score to accurately recognize data belonging to the known classes, making our method well-suited for handling uncurated data in deployment. We show through extensive experimental evaluations on several datasets that our method shows overall unmatched robustness and performance in terms of closed-set accuracy and open-set recognition compared with state-of-the-art for OSSL. Our code will be released upon publication.

翻译：开放的半监督学习(OSSL)是一个现实的半监督学习环境,无标签的培训集包含在标签数据集中不存在的班级。许多现有的OSSL方法假定这些分配外数据有害,并努力将数据从未知的班级中排除出来,而我们则提议一个开放的OSSL框架,便利通过自监督查看从所有未标签数据中学习。此外,我们利用基于能源的分数准确识别属于已知班级的数据,使我们的方法适合于在部署中处理未精确的数据。我们通过对几个数据集的广泛实验评估表明,我们的方法在封闭设置准确性和开放的识别方面,与开放源码软件的状态相比,总体上没有匹配的可靠性和性能。我们的代码将在公布时公布。</s>